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Remote Sensing
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Remote Sensing in Safety and Disaster Prevention Engineering
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Remote Sensing in Safety and Disaster Prevention Engineering

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 3890

Special Issue Editors

Prof. Dr. Yang Yang

E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing Univerity, Chongqing, China
Interests: bridge and structure inspection and reinforcement; structural health monitoring; structural vibration; seismic evaluation for structure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiansheng Fan

E-Mail Website
Guest Editor
Department of Civil Engineering, Tsinghua University, Beijing, China
Interests: steel-concrete composite structure; bridge engineering; structural inspection and assessment
Prof. Dr. Giuseppe Lacidogna

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Interests: nondestructive testing (NDT); acoustic emission; electromagnetic emission; critical phenomena in structural mechanics; critical phenomena in geophysics; fracture mechanics; static and dynamic analysis of high-rise buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large-scale urban construction is popular globally. Due to the incomplete and in-depth research on urban construction in many cities and towns, and the lack of sufficient remote sensing technology considerations, the rapid development of urban construction has led to a variety of urban disasters. The types, quantity and scope of urban disasters are expanding, so it is objectively required that we solve the problems in urban construction because we only pay attention to the speed and scale of development, and ignore the relationship between remote sensing and safety and disaster prevention in the development process.

Safety and disaster prevention engineering concerns diverse critical perspectives on all dimensions of engineering disasters. The rapid development of the safety and disaster prevention stimulates the innovation in strategic emerging industries, and reduces the loss in various disasters. Remote sensing is frequently used in the safety and disaster prevention engineering like model construction of civil engineering.

In this Special Issue on remote sensing, we mainly discuss the application of remote sensing in various safety and disaster prevention engineering, including but not limited to seismic engineering structures research; wind engineering and structural safety research; structural health monitoring and safety evaluating; fire safety of structures; disaster prevention mechanism; disaster management system architecture; structure safety analysis method; materials, bridges, rock-soil, municipal and water conservancy protection and diagnosis technology, etc.

Prof. Dr. Yang Yang
Prof. Dr. Jiansheng Fan
Prof. Dr. Giuseppe Lacidogna
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • safety and disaster prevention engineering
  • seismic engineering
  • wind engineering
  • structural health monitoring
  • fire safety
  • protection and diagnosis technology

Published Papers (3 papers)

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Research

20 pages, 3239 KiB  
Article
Statistical Seismic Analysis by b-Value and Occurrence Time of the Latest Earthquakes in Italy
by Giuseppe Lacidogna, Oscar Borla and Valentina De Marchi
Remote Sens. 2023, 15(21), 5236; https://doi.org/10.3390/rs15215236 - 03 Nov 2023
Viewed by 895
Abstract
The study reported in this paper concerns the temporal variation in the b-value of the Gutenberg–Richter frequency–magnitude law, applied to the earthquakes that struck Italy from 2009 to 2016 in the geographical areas of L’Aquila, the Emilia Region, and Amatrice–Norcia. Generally, the [...] Read more.
The study reported in this paper concerns the temporal variation in the b-value of the Gutenberg–Richter frequency–magnitude law, applied to the earthquakes that struck Italy from 2009 to 2016 in the geographical areas of L’Aquila, the Emilia Region, and Amatrice–Norcia. Generally, the b-value varies from one region to another dependent on earthquake incidences. Higher values of this parameter are correlated to the occurrence of low-magnitude events spread over a wide geographical area. Conversely, a lower b-value may lead to the prediction of a major earthquake localized along a fault. In addition, it is observed that each seismic event has a different “occurrence time”, which is a key point in the statistical study of earthquakes. In particular, its results are absolutely different for each specific event, and may vary from years to months or even just a few hours. Hence, both short- and long-term precursor phenomena have to be examined. Accordingly, the b-value analysis has to be performed by choosing the best time windows to study the foreshock and aftershock activities. Full article
(This article belongs to the Special Issue Remote Sensing in Safety and Disaster Prevention Engineering)
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17 pages, 7066 KiB  
Article
Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion
by Kun Zeng, Sheng Zeng, Hai Huang, Tong Qiu, Shihui Shen, Hui Wang, Songkai Feng and Cheng Zhang
Remote Sens. 2023, 15(13), 3444; https://doi.org/10.3390/rs15133444 - 07 Jul 2023
Viewed by 1258
Abstract
Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout [...] Read more.
Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements. Full article
(This article belongs to the Special Issue Remote Sensing in Safety and Disaster Prevention Engineering)
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Graphical abstract

26 pages, 7381 KiB  
Article
Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques
by Bahareh Behkamal, Alireza Entezami, Carlo De Michele and Ali Nadir Arslan
Remote Sens. 2023, 15(12), 3095; https://doi.org/10.3390/rs15123095 - 13 Jun 2023
Cited by 5 | Viewed by 1088
Abstract
Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a [...] Read more.
Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability. Full article
(This article belongs to the Special Issue Remote Sensing in Safety and Disaster Prevention Engineering)
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